# ProSky: NEAT Meets NOMA-mmWave in the Sky of 6G

arXiv: https://arxiv.org/abs/2210.11406 · HTML: https://mlsanity.com/paper/2210.11406/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 19 references that are in the map.
- First posted on arXiv 13 Oct 2022.

## Builds on (most cited first, 7 of 7)

- [Dueling Network Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1511.06581/) (2015)
- [Millimeter Wave Channel Modeling and Cellular Capacity Evaluation](https://mlsanity.com/paper/1312.4921/) (2013)
- [QuaRL: Quantization for Fast and Environmentally Sustainable Reinforcement Learning](https://mlsanity.com/paper/1910.01055/) (2019)
- [UAV Communications Based on Non-Orthogonal Multiple Access](https://mlsanity.com/paper/1809.05767/) (2018)
- [How Crucial Is It for 6G Networks to Be Autonomous?](https://mlsanity.com/paper/2106.06949/) (2021)
- [On the Performance of Non-Orthogonal Multiple Access (NOMA): Terrestrial vs. Aerial Networks](https://mlsanity.com/paper/2002.00826/) (2020)
- [AdaptSky: A DRL Based Resource Allocation Framework in NOMA-UAV Networks](https://mlsanity.com/paper/2107.01004/) (2021)

## Abstract (arXiv)

Rendering to their abilities to provide ubiquitous connectivity, flexibly and cost effectively, unmanned aerial vehicles (UAVs) have been getting more and more research attention. To take the UAVs' performance to the next level, however, they need to be merged with some other technologies like non-orthogonal multiple access (NOMA) and millimeter wave (mmWave), which both promise high spectral efficiency (SE). As managing UAVs efficiently may not be possible using model-based techniques, another key innovative technology that UAVs will inevitably need to leverage is artificial intelligence (AI). Designing an AI-based technique that adaptively allocates radio resources and places UAVs in 3D space to meet certain communication objectives, however, is a tough row to hoe. In this paper, we propose a neuroevolution of augmenting topologies NEAT framework, referred to as ProSky, to manage NOMA-mmWave-UAV networks. ProSky exhibits a remarkable performance improvement over a model-based method. Moreover, ProSky learns 5.3 times faster than and outperforms, in both SE and energy efficiency EE while being reasonably fair, a deep reinforcement learning DRL based scheme. The ProSky source code is accessible to use here: https://github.com/Fouzibenfaid/ProSky